The growing cultural relevance of data literacy requires a deeper acknowledgement and assessment of the role that data visualization can play if interpreted as a mean to increase the quality of information besides reporting its quantitative features, being therefore actively integrated within the current ecosystem of knowledge communication. In fact, while data visualization is often considered as a merely quantitative tool for information analysis and reporting, the methodologies underpinning this particular branch of visual communication go far beyond the simple restitution of numbers and sizes, frequently approaching more qualitative stances. Highlighting an implicit correlation between data visualization and qualitative research, the present contribution proposes an interpretative framework based on a set of five comparative analogies, in relation to which a same number of emergent fields of application for data visualization are identified, described, and contextualized.
Data visualization as a qualitative driver in knowledge communication: an interpretative framework
Ciliberto, Giulia
2021-01-01
Abstract
The growing cultural relevance of data literacy requires a deeper acknowledgement and assessment of the role that data visualization can play if interpreted as a mean to increase the quality of information besides reporting its quantitative features, being therefore actively integrated within the current ecosystem of knowledge communication. In fact, while data visualization is often considered as a merely quantitative tool for information analysis and reporting, the methodologies underpinning this particular branch of visual communication go far beyond the simple restitution of numbers and sizes, frequently approaching more qualitative stances. Highlighting an implicit correlation between data visualization and qualitative research, the present contribution proposes an interpretative framework based on a set of five comparative analogies, in relation to which a same number of emergent fields of application for data visualization are identified, described, and contextualized.File | Dimensione | Formato | |
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